Using the Criterion-Predictor Factor Model to Compute the Probability of Detecting Prediction Bias with Ordinary Least Squares Regression

Research output: Contribution to journalArticlepeer-review

Abstract

The study of prediction bias is important and the last five decades include research studies that examined whether test scores differentially predict academic or employment performance. Previous studies used ordinary least squares (OLS) to assess whether groups differ in intercepts and slopes. This study shows that OLS yields inaccurate inferences for prediction bias hypotheses. This paper builds upon the criterion-predictor factor model by demonstrating the effect of selection, measurement error, and measurement bias on prediction bias studies that use OLS. The range restricted, criterion-predictor factor model is used to compute Type I error and power rates associated with using regression to assess prediction bias hypotheses. In short, OLS is not capable of testing hypotheses about group differences in latent intercepts and slopes. Additionally, a theorem is presented which shows that researchers should not employ hierarchical regression to assess intercept differences with selected samples.

Original languageEnglish (US)
Pages (from-to)561-580
Number of pages20
JournalPsychometrika
Volume77
Issue number3
DOIs
StatePublished - Jul 2012

Keywords

  • measurement bias
  • power
  • prediction bias
  • selection
  • type I error

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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